Systems and methods of determining parameters of a marine seismic survey

ABSTRACT

Systems and methods of detecting marine seismic survey parameters are provided. A data processing system can obtain seismic data from seismic data acquisition units disposed on a seabed responsive to an acoustic signal propagated from an acoustic source through a water column. The data processing system can determine from the seismic data, a direct arrival time for the acoustic signal at each of the plurality of seismic data acquisition units, and can obtain an estimated depth value of each of the plurality of seismic data acquisition units and an estimated water column transit velocity of the acoustic signal. The data processing system can apply a depth model and a water column transit velocity model to the estimated depth value and to the estimated water column transit velocity determine an updated depth value and an updated water column transit velocity for each of the plurality of seismic data acquisition units.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority under 35 U.S.C. § 120 asa continuation of U.S. patent application Ser. No. 16/790,272, filedFeb. 13, 2020, which is a continuation of U.S. patent application Ser.No. 16/375,714, filed Apr. 4, 2019, which is a continuation of U.S.patent application Ser. No. 14/864,345, filed Sep. 24, 2015, each ofwhich is hereby incorporated by reference herein in its entirety.

BACKGROUND

Seismic data may be evaluated to obtain information about subsurfacefeatures. The information can indicate geological profiles of asubsurface portion of earth, such as salt domes, bedrock, orstratigraphic traps, and can be interpreted to indicate a possiblepresence or absence of minerals, hydrocarbons, metals, or other elementsor deposits.

SUMMARY

At least one aspect is directed to a method of detecting seismic dataacquisition unit depth and acoustic signal water column transit velocityfor a seismic survey. The method can include obtaining, by a dataprocessing system, seismic data acquired by a plurality of seismic dataacquisition units disposed on a seabed responsive to an acoustic signalpropagated from an acoustic source through a water column. The methodcan include determining, by the data processing system, from the seismicdata, a direct arrival time for the acoustic signal at each of theplurality of seismic data acquisition units. The method can includeobtaining an estimated depth value of each of the plurality of seismicdata acquisition units and an estimated water column transit velocity ofthe acoustic signal. The method can include identifying an averagetravel time error value based on the direct arrival time for theacoustic signal at each of the plurality of seismic data acquisitionunits, the estimated depth value, and the estimated water column transitvelocity. The method can include determining an initial mean absolutedeviation of travel time error from the average travel time error value.The method can include applying a depth model and a water column transitvelocity model to the estimated depth value and to the estimated watercolumn transit velocity to determine, using the initial mean absolutedeviation of travel time error, an updated depth value and an updatedwater column transit velocity for each of the plurality of seismic dataacquisition units. The method can include creating, by the dataprocessing system, a data structure indicating the updated depth valuefor each of the plurality of seismic data acquisition units.

At least one aspect is directed to a system of detecting parametersrelated to a marine seismic survey. The system can include a dataprocessing system having a depth value generation module and a watercolumn transit velocity generation module. The data processing systemcan obtain seismic data acquired by a plurality of seismic dataacquisition units disposed on a seabed responsive to an acoustic signalpropagated from an acoustic source through a water column. The dataprocessing system can determine, from the seismic data, a direct arrivaltime for the acoustic signal at each of the plurality of seismic dataacquisition units. The data processing system can obtain an estimateddepth value of each of the plurality of seismic data acquisition unitsand an estimated water column transit velocity of the acoustic signal.The data processing system can identify an average travel time errorvalue based on the direct arrival time for the acoustic signal at eachof the plurality of seismic data acquisition units, the estimated depthvalue, and the estimated water column transit velocity. The dataprocessing system can determine an initial mean absolute deviation oftravel time error from the average travel time error value. The dataprocessing system can apply a depth model and a water column transitvelocity model to the estimated depth value and to the estimated watercolumn transit velocity determine an updated depth value and an updatedwater column transit velocity for each of the plurality of seismic dataacquisition units. The data processing system can create a datastructure indicating the updated depth value for each of the pluralityof seismic data acquisition units.

At least one aspect is directed to a computer readable storage mediumstoring instructions that when executed by one or more data processors,cause the one or more data processors to perform operations. Theoperations can include obtaining seismic data acquired by a plurality ofseismic data acquisition units disposed on a seabed responsive to anacoustic signal propagated from an acoustic source through a watercolumn as part of a seismic survey. The operations can includedetermining, from the seismic data, a direct arrival time for theacoustic signal at each of the plurality of seismic data acquisitionunits, and obtaining an estimated depth value of each of the pluralityof seismic data acquisition units and an estimated water column transitvelocity of the acoustic signal. The operations can include identifyingan average travel time error value based on the direct arrival time forthe acoustic signal at each of the plurality of seismic data acquisitionunits, the estimated depth value, and the estimated water column transitvelocity. The operations can include determining an initial meanabsolute deviation of travel time error from the average travel timeerror value. The operations can include determining an updated depthvalue and an updated water column transit velocity for each of theplurality of seismic data acquisition units. The operations can includecreating a data structure indicating the updated depth value for each ofthe plurality of seismic data acquisition units.

These and other aspects and implementations are discussed in detailbelow. The foregoing information and the following detailed descriptioninclude illustrative examples of various aspects and implementations,and provide an overview or framework for understanding the nature andcharacter of the claimed aspects and implementations. The drawingsprovide illustration and a further understanding of the various aspectsand implementations, and are incorporated in and constitute a part ofthis specification.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are not intended to be drawn to scale. Likereference numbers and designations in the various drawings indicate likeelements. For purposes of clarity, not every component may be labeled inevery drawing. In the drawings:

FIG. 1 is a schematic diagram depicting an example system for detectingparameters related to marine seismic survey, according to anillustrative implementation;

FIG. 2 is a block diagram depicting an example environment for detectingparameters related to marine seismic survey, according to anillustrative implementation;

FIG. 3 is a flow diagram depicting a method of detecting seismic dataacquisition unit depth and acoustic signal water column transitvelocity, according to an illustrative implementation; and

FIG. 4 is a block diagram illustrating an architecture for a computersystem that can be employed to implement the systems and methodsdescribed and illustrated herein.

DETAILED DESCRIPTION

Following below are more detailed descriptions of various conceptsrelated to, and implementations of, methods, apparatuses, and systems ofdetermining or estimating the depth of one or more receivers such asseismic data acquisition units associated with a seismic survey, as wellas determining or estimating water column transit velocity of anacoustic or other signal that propagates to or from a seismic sourcethrough a water column. The various concepts introduced above anddiscussed in greater detail below may be implemented in any of numerousways.

Systems and method of the present disclosure relate generally todetermining or detecting parameters such as seismic data acquisitionunit depth or acoustic signal water column transit velocity related to amarine seismic survey. The data processing system described herein candetermine seismic data acquisition unit depth for a plurality of seismicdata acquisition units, and can determine the water column transitvelocity of one or more acoustic signals from (or sometimes exclusivelyfrom) seismic data that can include timing information. By applyingdepth and water column transit velocity models to the seismic data, thedata processing system can determine seismic data acquisition unit depthand acoustic signal water column transit velocity from or simultaneouslyfrom the seismic data, in the presence of timing errors for any givenseismic data acquisition unit, and without zero mean error assumptionson a starting model for receiver depth or water velocity that could forexample be present with traveltime inversion techniques. For example,unit depth errors using traveltime inversion techniques may not be zeromean error based due to depth variances associated with an uneven seabed135 in an area of a seismic survey.

FIG. 1 depicts a system 100 for detecting parameters related to a marineseismic survey. The system 100 can include at least one vessel 105, suchas a ship that implements a marine seismic survey. The vessel 105 cantravel through a body of water 110, in direction of motion 115, forexample. The body of water 110 can include fresh water, salt water,mixed water, volumes of oceans, seas, or lakes, or mixed or transitionzones such as inlets, deltas, or bays. The vessel 105 can include, beequipped with, or tow at least one seismic source 120 through the bodyof water 110.

The seismic sources 120 can include at least one air gun or other devicethat generates seismic energy. The seismic source(s) 120 can be towedbehind or positioned underneath the vessel 105, and disposed under thesurface of the body of water 110. For example, at least one cable 125can tow the seismic source 120 behind the vessel at a depth of between 2and 80 meters below the surface of the body of water 110. In oneimplementation the seismic source depth is 10 meters below the surfaceof the both of water 110. The seismic source 120 can actuate to produceat least one pulse of acoustic energy (e.g. at least one acousticsignal) that propagates from the seismic source 120 through the watercolumn 130, where it passes through the seabed 135 and penetratesbeneath the surface of the earth. The acoustic energy can reflect orrefract off of subsurface features, such as geologic boundaries orlayers. The reflected or refracted signals can be detected or recordedas seismic data by at least one seismic data acquisition unit 140disposed on the seabed 135. In some implementations, a plurality ofseismic sources 120 are disposed in an array of individual air guns ofvarying sizes, disposed at the same or varying depths, that can besimultaneously actuated to generate the acoustic signal or sequentiallyactuated to generate multiple acoustic signals. Further, multiple cables125 can attach multiple seismic sources 120 to the vessel 105. Forexample, the vessel 105 can tow multiple cables 125 in parallel withseismic sources 120 arranged in a parallel line or grid configurationand attached to the cables 125.

The system 100 can include a plurality of seismic data acquisition units140. The seismic data acquisition units 140 can each include at leastone geophone or hydrophone to detect the seismic data. The seismic dataacquisition units 140 can each include at least one power source,memory, hydrophone, geophone, and communication interface to transmitthe seismic data out from the seismic data acquisition unit 140. Theseismic data acquisition units 140 can be deployed from the vessel 105that is associated with the seismic source, or from a different vesselto the seabed 135. One or more remote operating vehicle (ROV) can assistwith deployment and collection of the seismic data acquisition units140. For example, the ROV can be a submersible autonomous vehicle thatcan place or collect the seismic data acquisition units 140 on or fromthe seabed 135. The seismic data acquisition units 140 can be disposedin an array such as a grid pattern (e.g., a symmetric formation) on theseabed 135. Each seismic data acquisition unit 140 can collect and storeseismic data. Upon retrieval, for example by an ROV or via a cable, theseismic data can be extracted or obtained from the seismic dataacquisition units 140. For example the seismic data can be transmittedor manually extracted from the seismic data acquisition units 140. Theseismic data can be interpreted to identify geologic boundaries orformations that may indicate the presence of minerals, hydrocarbons, orvarious earth elements. In addition to ROV or manual seismic dataextraction, the seismic data acquisition units 140 can also transmit theacquired seismic data to a database or data processing system via wiredcommunication using at least one data communications cable connected tothe seismic data acquisition unit, or via wireless communication such aslaser or optic based subsurface communications through the water column130 between seismic data acquisition units 140 and additional relay,computing, or data storage devices.

Referring to FIG. 2, among others, the system 100 can include at leastone data processing system 205, such as at least one logic device suchas a computing device, server, personal computer, laptop, desktop,tablet, mobile, personal digital assistant, or smartphone computingdevice having at least one processor. The data processing system 205 canbe located on the vessel 105, or separate from the vessel 105 in a landbased facility. The data processing system 205 can include at least oneserver. For example, the data processing system 205 can include aplurality of servers located in at least one data center or server farm.The data processing system 205 can estimate or otherwise determineseismic data acquisition unit depth and acoustic signal water columntransit velocity from the seismic data. In some implementations, thedata processing system 205 estimates or determines the seismic dataacquisition unit depth and acoustic signal water column transit velocityexclusively from the seismic data, which includes timing informationrelated to the acoustic signal.

The data processing system 205 can include at least one water columntransit velocity generation module 210, at least one depth valuegeneration module 215, and at least one database 220. The water columntransit velocity generation module 210 and the depth value generationmodule 215 can each include at least one processing unit, server,virtual server, circuit, engine, agent, appliance, or other logic devicesuch as programmable logic arrays configured to communicate with thedatabase 220 and with other computing devices 225 (e.g., other laptop,personal, desktop, tablet, or smartphone computing devices via acomputer network). The database 220 can store the seismic data obtainedfrom the seismic data acquisition units 140 as well as data generated bythe water column transit velocity generation module 210 and the depthvalue generation module 215, for example.

The water column transit velocity generation module 210 and the depthvalue generation module 215 can include or execute at least one computerprogram or at least one script. The water column transit velocitygeneration module 210 and the depth value generation module 215 can beseparate components, a single component, or part of the data processingsystem 205. The water column transit velocity generation module 210 andthe depth value generation module 215 can include combinations ofsoftware and hardware, such as one or more processors configured toobtain and evaluate seismic data from the seismic data acquisition units140, determine direct arrival times for the acoustic signal at one ormore of the seismic data acquisition units 140, obtain estimated depthvalues of one or more of the seismic data acquisition units 140, obtainestimated water column transit velocities of one or more of the seismicdata acquisition units 140, identify average travel time error values ofthe seismic data and initial mean absolute deviation values of theaverage travel time error values, apply depth models and water columntransit velocity models to the estimated depth value and the estimatedwater column transit velocity to determine updated depth values andupdated water column velocities, and to create one or more datastructures indicating the updated depth values or updated water columnvelocities, for example.

The data processing system 205 can include servers or end user computingdevices configured to display data (directly at a monitor or indirectlyby communicating via a computer network to another computing device suchas the computing device 225) such as the updated depth value of one ormore seismic data acquisition units 140 or the updated water columntransit velocity of the acoustic signal, as well as other data such asinterpretations of the seismic data indicating the presence or absenceof geologic boundaries, layers, formations, mineral or hydrocarbondeposits, or other conditions such as salt domes. The data processingsystem 205 can include desktop computers, laptop computers, tabletcomputers, smartphones, personal digital assistants, mobile devices, enduser computing devices, consumer computing devices, servers, clients,and other computing devices. The data processing system 205 can includeuser interfaces such as microphones, speakers, touchscreens, keyboards,pointing devices, a computer mouse, touchpad, or other input or outputinterfaces.

The data processing system 205 can include the water column transitvelocity generation module 210 or the depth value generation module 215as part of one or more servers of a seismic data interpretation systemto receive seismic data from the seismic data acquisition units 140directly, manually (via a memory stick, disk, or removable mediatransferred from the seismic data acquisition units 140 to the dataprocessing system 205) or via a wired or wireless network such as theinternet or local, wide, or metro area network, and to provide outputdata such as updated depth values that indicate the subsurface depth ofthe seismic data acquisition units 140 when disposed on the seabed 135,as well as updated water column transit velocity values that indicate avelocity of one or more acoustic signals through the water column 130from the seismic source 120 to the seismic data acquisition units 140 orvice-versa.

The water column transit velocity generation module 210 or the depthvalue generation module 215 can be part of, or can include scriptsexecuted by, one or more servers in the data processing system 205(e.g., a search engine system) to determine depth values and watercolumn transit velocity values. The water column transit velocitygeneration module 210 can be part of the same computing device or adifferent computing device as the depth value generation module 215 inthe data processing system 205. From the seismic data, the water columntransit velocity generation module 210 can determine water columntransit velocity values, and can refine or update those values. Alsofrom the seismic data, the depth value generation module 215 candetermine depth values of the seismic data acquisition units 140, andcan update those values.

The data processing system 205 can include multiple servers or othercomputing devices (laptop, desktop, mobile, tablet, or smartphonecomputing device) in communication with each other via a network. Thenetwork can include computer networks such as the internet, local, wide,metro or other area networks, intranets, satellite networks, othercomputer networks such as voice or data mobile phone communicationnetworks, and combinations thereof.

In some implementations, the data processing system 205 can obtain theseismic data. For example, the vessel 105 can tow at least one seismicsource 120. The seismic source 120 can be actuated to generate at leastone acoustic signal that propagates through the water column 130. Atleast some of the acoustic signal can pass through the seabed 135, downinto the earth, and reflect or refract off of subsurface geologicformations, back (e.g., up) toward the surface of seabed 135 (e.g., theocean floor). The seismic data acquisition units 140 can detect,collect, acquire, or record this reflected or refracted seismic data.The seismic data can then be transferred (e.g., via wired, wireless,manual, optical, or direct transmissions) from the seismic dataacquisition units 140 to the data processing system 205. The dataprocessing system 205 can obtain the seismic data while present on thevessel 105 (e.g., as a computing device disposed in a control station ofthe vessel 105) or at a land based location. Thus, the data processingsystem 205 can obtain the seismic data that was first acquired by theseismic data acquisition units 140 responsive to propagation of theacoustic signal from the acoustic source through the water column. Theseismic data can include timing data related to actuation of the seismicsource 120 or related to time of acquisition of the seismic data by theseismic data acquisition units 140.

From the seismic data, the data processing system 205 can determine adirect arrival time for the acoustic signal at each or at least one ofthe seismic data acquisition units 140. The direct arrival timeindicates a time period from generation of the acoustic signal todetection of the seismic data by a seismic data acquisition unit 140.The data processing system 205 can determine the direct arrival timeusing a first break picking detection technique. For example, the dataprocessing system 205 can determine the direct arrival time bydetermining the time of maximum amplitude of the seismic wavelet afterapplying a source designature. The seismic data analyzed by the dataprocessing system 205 to determine the direct arrival time can be a rawwavelet that is unprocessed or unchanged from the seismic data asacquired by the seismic data acquisition unit 140, or can be filtered toremove minimum phase or zero phase occurrences.

The data processing system 205 (or component thereof such as the depthvalue generation module 215) can determine an estimated depth value ofeach of the plurality of seismic data acquisition units 140. Forexample, the estimated depth value can be an initial estimated depth ofa seismic data acquisition unit 140 disposed on the seabed 135 at thebottom of the water column 130. The data processing system 205 canupdate this initial estimated depth value to determine a more preciseupdated depth value as described herein. In some implementations, aremote operated vehicle (ROV) or autonomous underwater vehicle (AUV),(e.g., an unmanned submarine) equipped with a pressure sensor or otherdepth sensor can pass generally near (e.g., within 50 meters) seismicdata acquisition units 140 and based on water pressure or othermeasurement can determine a depth. The data processing system 205 canobtain this information (or determine the estimated depth from pressureor other information received from the ROV or AUV or from the seismicdata acquisition units 140 themselves) and can assign depth values toeach seismic data acquisition unit 140. The data processing system 205can also determine the estimated depth values for each of the seismicdata acquisition units 140 from data obtained via echo sounding, sonar,or fathometer techniques that are implemented from the vessel 105, orfrom preexisting data, e.g., obtained from the database 220. Theestimated depth value of the seismic data acquisition units 140 can bewithin 100 meters of the actual depth of the seismic data acquisitionunits 140. In some implementations, the estimated depth value is within50 meters of the actual depth value of the seismic data acquisitionunits.

The water column transit velocity generation module 210 or othercomponent of the data processing system 205 can obtain at least oneestimated water column transit velocity of the acoustic signal. Theestimated water column transit velocity can be an initial estimatedtransit velocity of the acoustic signal through the water column 130.The estimated water column transit velocity can be a predetermined(e.g., rather than measured) velocity. For example, the estimated watercolumn transit velocity can be a predetermined time constant and depthinvariant water velocity of 1500 meters/second, although other valuesabove and below this value can be used. The data processing system 205can update this initial estimated water column transit velocity todetermine a more precise updated water column transit velocity asdescribed herein.

The data processing system 205 can identify or determine an averagetravel time error value for the acoustic signal(s) at each of theplurality of seismic data acquisition units 140, based on the directarrival time of the acoustic signal at each of the plurality of seismicdata acquisition units 140 and based on the estimated depth values andestimated water column transit velocity of the acoustic signal(s).

Travel time error can be the difference between a modeled travel timeand the actual picked travel time, or direct arrival time, representedfor example by the following equation:

${T_{error}( {s,r,t} )} = {\frac{\sqrt{( {s_{x} - r_{x}} )^{2} + ( {s_{y} - r_{y}} )^{2} + ( {s_{z} - r_{z}} )^{2}}}{v( {t,r_{z}} )} - {{DA}( {s,r} )}}$

In this equation s is a unique source, r is a unique receiver. Thecoordinates s_(x), s_(y), and s_(z) are the modeled source X Y and Zcoordinates. The coordinates r_(x), r_(y), and r_(z) are the modeledreceiver X Y and Z coordinates, in the case of this process r_(z) isvariable. The variable v(t, r_(z)) is the time and depth variablevelocity function at the time of the acoustic signal (e.g., shot) anddepth of the receiver. DA(s,r) is the picked direct arrival time for theunique source receiver pair.

For example, the data processing system 205 can determine the traveltime error for individual seismic data acquisition units 140 thatindicates a time difference between the determined direct arrival timefor a given seismic data acquisition unit 140 (determined from theseismic data) and a model arrival time based on the estimated depthvalue and the estimated water column transit velocity for that givenseismic data acquisition unit 140. The data processing system 205 canaverage the travel time error values of each seismic data acquisitionunit 140 to identify or determine the average travel time error valuefor a seismic survey or portion thereof such as one or more acousticsignals propagated from at least one seismic source 120.

From the average travel time error value, the data processing system 205can determine an initial mean absolute deviation of travel time error.The initial mean absolute deviation can be determined from the traveltime error of the initial model of a given vessel pass, using theinitially defined velocity and receiver depth. The mean absolutedeviation can be the average of the absolute value of the differencebetween the travel time errors and the average travel time error,represented for example by the following equations, whereT_(error)(s,r,t) is the travel time error for a given trace (uniquesource receiver pair), and n is the number of traces in the grouping oftraces analyzed:

${Error_{mean}} = \frac{\sum{T_{error}( {s,r,t} )}}{n}$

${MeanAbsoluteDeviation}{= \frac{\sum{{{T_{error}( {s,r,t} )} - {Error_{mean}}}}}{n}}$

This can be applied to a sail line, source pass, or any other collectionof source locations that are reasonably close in spatial location andtime of acquisition, within the offset range selected for analysis. Insome implementations, the initial mean absolute deviation is determined,rather than standard deviation, as the travel time error values may nothave a standard distribution. The data processing system 205 can updatethis initial mean absolute deviation of travel time error to determine amore precise mean absolute deviation of travel time error value.

Thus, the data processing system 205 can determine or obtain the(initial) estimated depth value and (initial) estimated water columntransit velocity associated with each seismic data acquisition unit 140,as well as an initial absolute deviation of travel time error. With theestimated depth value, the estimated water column transit velocity, andthe initial mean absolute deviation of travel time error, the dateprocessing system 205 can determine an updated depth value and anupdated water column transit velocity for each of the acousticsignal/seismic data acquisition unit 140 pairs. The updated receiverdepth and water column transit velocity can be selected from apredetermined grid of variances of the initial modelled values. The gridcan have regular variations, random variations, or pseudo randomvariations. For example, the updated depth value and water columntransit velocity can selected from the pair that results in the lowestmean absolute deviation of error of all pairs iterated.

For example, the data processing system 205 or components such as thedepth value generation module 215 and the water column transit velocitymodule 210 can apply a depth model and a water column transit velocitytransit model to the estimated depth value and to the estimated watercolumn transit velocity model, respectively, to determine, using theinitial mean absolute deviation of travel time error, an updated depthvalue for all or at least one of the seismic data acquisition units 140and an updated water column transit velocity of at least one acousticsignal to or from at least one of the seismic data acquisition units140.

The process of updating the estimated depth value and the estimatedwater column transit velocity for associated with the seismic source(s)120 and seismic data acquisition units 140 can include iterativecalculations of depth values and water column velocities for the seismicsource 120 (or for the acoustic signals emanating from those sources)and the seismic data acquisition units 140. For example, the estimateddepth value can be adjusted by a predetermined amount such as 1.5meters, and the estimated water column transit velocity of the acousticsignal can be adjusted by a predetermined velocity such as 0.5meters/second. These values are examples and other values above andbelow these values can be used. With these adjusted values, the dataprocessing system 205 can determine new or updated depth values andwater column transit velocity. For example, an average travel time errorvalue that indicates an average of the differences between the directarrival time (from the obtained seismic data) and the modeled arrivaltime based on the values adjusted by 1.5 m and 0.5 m/s can be determinedfor a series of acoustic signals (e.g., from a source pass of theseismic source 120) and each seismic data acquisition unit 140. The dataprocessing system 205 can determine a new mean absolute deviation fromthis (new) average travel time error value for the given depth/velocitypair adjusted by 1.5 m and 0.5 m/s. The data processing system 205 cancompare the new mean absolute deviation of travel time error (that isbased on the adjusted values) with the initial mean absolute deviation(based on the original values). When the new mean absolute deviation oftravel time error is less than (and therefore better than) the initial(or other previous) mean absolute deviation the data processing system205 can update the water column transit velocity with the new value(e.g., adjusted by 0.5 m/s), or can update the estimated depth value ofa seismic data acquisition unit 140 with the new value (e.g., adjustedby 1.5 m). This example results in a refinement of the originallyestimated depth and water column transit velocity values.

The updated values can be referred to as the best values at this stagein the iterative process. This process can repeat in an iterativemanner. For example, the data processing system 205 can adjust the depthvalue of a seismic data acquisition unit 140 by another 1.5 m, and thewater column transit velocity by another 0.5 m/s. From this data another(newer) mean absolute deviation of travel time error can be determinedand compared with the previous or best mean absolute deviation of traveltime error. If the newer mean absolute deviation of travel time error isless than the previous or best absolute deviation of travel time error,the data processing system 205 can again update the water column transitvelocity or can again update the estimated depth value. If the newermean absolute deviation of travel time error is greater than or equal tothe previous or best absolute deviation of travel time error, the dataprocessing system 205 can discard the newer mean absolute deviation oftravel time error (or associated depth value or water column transmitvelocity) and continue to use the previous best values. The dataprocessing system 205 can execute any number of these iterations. Forexample, the data processing system 205 can run 41 iterations where theestimated water column transit velocity or estimated depth value areadjusted 41 times at predetermined step values of 1.5 m for theestimated depth values of 0.5 m/s for the estimated water column transitvelocity.

In some implementations, having determined the initial mean absolutedeviation of travel time error, the data processing system 205 applies adepth model (e.g., using the depth value generation module 215) andapplies a water column transit velocity generation model (e.g., usingthe water column transit velocity generation module 210) to implementone or more of the following operations to update the estimated depthvalue and the estimated water column transit velocity model:

1.) Identify the step size or adjustment value from the (e.g., initial)estimated values of the depth value (e.g., 1.5 m) and of the watercolumn transit velocity (e.g., 0.5 m/s) and a number of iterations(e.g., 41 iterations at these step sizes for both the depth value andthe water column transit velocity). These may be referred to as themodel values.

2.) Determine the travel time error value between the direct arrivaltime determined from the seismic data and the direct arrival timedetermined from the adjusted values.

3.) Determine the average travel time error with the adjusted (orupdated) depth value and water column transit velocity for a source passor series of acoustic signals for each seismic data acquisition unit140.

4.) Determine the (updated) mean absolute deviation from the averagetravel time error value for each adjusted depth value/water columntransit velocity pair using the adjusted values.

5.) Compare the updated mean absolute deviation from the average traveltime error value with the initial mean absolute deviation of travel timeerror from the average travel time error value. When the updated meanabsolute deviation is less than the initial mean absolute deviation (orless than a previous best mean absolute deviation value), the dataprocessing system 205 assigns or classifies the adjusted depth value asthe (best available) updated depth value, or the data processing systemassigns or classifies the adjusted water column transit velocity as the(best available) updated water column transit velocity for at least oneseismic data acquisition unit 140. When the updated mean absolutedeviation is greater than or equal to the initial mean absolutedeviation (or to a previous best mean absolute deviation value), thedata processing system 205 does not modify the initial, estimated, orpreviously updated best depth value and water column transit velocityfor at least one seismic data acquisition unit 140. In this latterexample, the data processing system 205 can discard the updated(greater) depth or values associated with the updated mean absolutedeviation.

6.) Repeat operations 1-5 for a number of iterations. For example, thedata processing system 205 can adjust the depth value and water columntransit velocity value at the 1.5 m and 0.5 m/s steps, respectively, 41times (or any other number) so that, in this example, 41 potential depthvalues and 41 potential water column transit velocities are evaluatedfor each acoustic signal and each seismic data acquisition unit 140,which may be referred to as a 41×41 grid.

In the above example, the data processing system 205 has determined anupdated depth value and updated water column transit velocity for atleast one (or for all) of the seismic data acquisition units 140 using,for example, the coarse adjustment values of 1.5 m for depth values and0.5 m/s for water column transit velocity. In some implementations, thedata processing system 205 refines (or continues updating) these updatedvalues. For example, the data processing system 205 (and componentsthereof) can repeat operations 1-6 with smaller adjustment values of,for example, 0.15 m depth steps and 0.1 m/s velocity increments again ona 41×41 grid for all or some of the seismic data acquisition units 140.The depth model and the water column transit velocity transit model canbe a single model (or module) or separate models, and can include one ormode scripts, applications or code that can be, for example, accessedfrom the database 220 (or other memory) and executed by the dataprocessing system 205 to determine the updated depth values and updatedwater column transit velocity for a given source pass, or series ofacoustic signals from one pass of the vessel 105 and seismic source 120over the seismic data acquisition units 140.

The data processing system 205 can determine updated depth values andupdated water column transit velocity for the seismic data acquisitionunits 140 for multiple source passes, or multiple trips of the vessel105 and seismic source 120 over at least a portion of the seismic dataacquisition units 140. In some implementations, the depth valuegeneration module 215 (or other component of the data processing system205) can determine the average of the final or updated depth values froma series of source passes for each seismic data acquisition unit 140. Inthis example, the average receiver depth is determined from multiplesource 120/vessel 105 passes. For example, there may be depth changesbetween source passes due to tidal or current changes, or to variationsin the depths of the seismic sources 120 beneath the surface of the bodyof water 110. The water column transit velocity can then be furtherrefined or updated with the depth value fixed at the average depthvalue. For example, the data processing system 205 can fix the depthvalue at the average depth value, and starting with the estimated watercolumn transit velocity of 1500 m/s, can repeat the iterative process(e.g., operations 1.-6.) with the fixed average depth value and with 0.1m/s adjustments to the estimated water column transit velocity from the1500 m/s starting point or from the best previously determined watercolumn transit velocity. In one implementation, the data processingsystem 205 repeats this process for 201 iterations, although othervalues above and below this number are possible.

For example, the data processing system 205 can perform the followingoperations with the fixed average depth value to further refine theupdate water column transit velocity (e.g., by applying the depth/watercolumn transit velocity model(s):

1.) With the fixed average depth value (of at least one seismic dataacquisition unit 140) the data processing system 205 can determine thetravel time error value between the direct arrival time determined fromthe seismic data and the direct arrival time determined from the fixedaverage depth value and the adjusted water column transit velocity(e.g., adjusted by 0.1 m/s increments).

2.) Determine the average travel time error for at least one seismicdata acquisition unit 140 for a source pass over the seismic dataacquisition unit(s) 140 with the adjusted water column transit velocity.

3.) Determine the (new) mean absolute deviation from the average traveltime error for the given adjusted water column transit velocity.

4.) Compare the (new) mean absolute deviation from operation 3immediately above with the best previously determined mean absolutedeviation. When the new mean absolute deviation is less than the bestpreviously determined mean absolute deviation, then replace the bestpreviously determined mean absolute deviation with the new mean absolutedeviation. When the new mean absolute deviation is great than or equalto the best previously determined mean absolute deviation, then do notreplace the best previously determined mean absolute deviation.

5.) Repeat operations 1-4 immediately above for all grid values (e.g.,201 iterations with 0.1 m/s adjustments to the water column transitvelocity).

In the above example, the data processing system 205 can determine that,so far, the best updated water column transit velocity is the oneassociated with the best mean absolute deviation value.

The data processing system 205 can create one or more data structuresindicating the updated depth value or the updated water column transitvelocity for all or at least one of the seismic data acquisition units140. In some implementations, the data structure includes one or moreASCII files that, for example, identify individual seismic dataacquisition units 140 and indicate their associated updated depth valuesor updated water column transit velocity associated with one or moreacoustic signals. The data structure can be written to or read from thedatabase 220, and can be provided for display or rendering at one ormore computing devices 225, e.g., as a report. The computing devices 225can be part of the data processing system 205 or separate from the dataprocessing system 205. For example, the data processing system 205 cantransmit the data structure as a report to the computing device 225 viathe internet or other computer network for display by the computingdevice 225. The data structures described herein can include other textor character coding schemes, such as UTF-8 coding schemes.

The water column transit velocity of acoustic signals can change basedon factors such as time or the depth of the water column 130. In someimplementations, the water column transit velocity model includes a twolayer model, where the water column transit velocity of the acousticsignal through the deep layer of the water column 130 (e.g., greaterthan 1250 meters) is determined by the data processing system 205 to bea function of the depth of the water with no time dependent variation.In this example, the shallow layer (e.g., less than 1250 m) isdetermined by the data processing system 205 to have the majority or allof the time dependent velocity variation. In some implementations, thedata processing server 205 determines the water column transit velocityfor a deep layer of the water column 130. The deep layer velocity can bedetermined as a function of water depth, based, for example on thefollowing equation, where V₁₂₅₀ is the interval velocity, e.g., 1488.6m/sec, at water depth of 1250 m, and z is the water depth at which tocompute the interval velocity:V _(deep)(Z)=V ₁₂₅₀+(z−1250)*0.0166

In this example, the data processing system 205 can determine theaverage velocity of the deep layer as the average of V_(deep) (z) andV₁₂₅₀. This value can be referred to as the deep layer water columntransit velocity.

Using the deep layer water column transit velocity and the best updatedwater column transit velocity, the data processing system 205 orcomponent thereof such as the water column transit velocity generationmodule 210 can determine the shallow layer water column transitvelocity. For example, for a given source pass of the seismic source 120over at least some of the seismic data acquisition units 140, the dataprocessing system 205 can determine the average shallow layer velocityand the average time of the sail line.

The average time of the sail line can be the average of the time of thesources 120, each time of shot (e.g., actuation of seismic source(s)120) can be given a numerical time value (e.g., seconds from thebeginning of a year, or floating point day of year units that could beused). The data processing system 205 can determine the average shallowlayer velocity by first computing the shallow layer velocity for eachseismic data acquisition unit 140/sail line pair, and then averaging thevelocity for all receivers that recorded the sail line. For example, theshallow layer velocity can be determined by using the followingequation:

${{V_{shallow}(t)} = \frac{{{V_{T}( {z,t} )}*z} - {( {z - {1250}} )*{( {{V_{deep}(z)} + {V_{deep}( {1250} )}} )/2}}}{1250}}{{V_{avg}(t)} = \frac{\Sigma{V_{shallow}(t)}}{m}}$

In the above equation, V_(T)(z,t) is the total layer velocity determinedby the processing system in step 5 above; z is the depth of the receiverdetermined in the previous section; V_(deep)(z) is the depth dependentinterval velocity defined by the equation above; 1250 is the chosenthickness of the time variant water column transit velocity layer (othervalues can be used); t is the average time of the sail line; V_(avg)(t)is the average shallow layer velocity at time t; and m is the number ofseismic data acquisition units 140 (e.g., receivers) that recorded thesail line (e.g., seismic data during a source pass or vessel 105 pass),within the offset range selected for analysis.

The above equation is an example to compute the shallow layer velocityfrom a known total layer velocity and a known deep layer, and the dataprocessing system 205 can use other methods to determine the shallowlayer velocity. Further, this process need not be applied only to deep(e.g., greater than 1250 m depth) seismic data acquisition units 140, asthe main assumption is that the deep layer velocity either does notchange as a function of time or varies slowly. For example, the secondpossibility is a reasonable assumption in most water exceeding about 400m of depth. In areas where the water depth to the receivers is less than1250 m the equations to compute the deep layer can change, or bereplaced with water velocity measurements made by instruments placed onthe ROVs or other devices.

The data processing system 205 can create at least one data structure(e.g., an ASCII file) indicating each average sail line time and shallowlayer water column transit velocity. Data structures generated by thedata processing system 205 (or obtained from the database 220) can beprovided for display as a report by the data processing system 205 or bethe computing device 225.

In some implementations, the data processing system 205 can identify ordetermine a first layer (e.g., deep) water column transit velocity forthe acoustic signal and each of the plurality of seismic dataacquisition units 140. The data processing system 205 can also identifyor determine a second layer water (e.g., shallow) column transitvelocity for the acoustic signal and each of the seismic dataacquisition units 140 based on the first layer water column transitvelocity and based on the updated water column transit velocity.

The data processing system 205 can also determine an average secondlayer (e.g., deep) water column transit velocity for at least oneacoustic signal, for example by averaging the second layer water columntransit velocities of an acoustic signal for a plurality of seismic dataacquisition units 140. The data processing system 205 can also identifya time invariant second layer water column velocity for the second(e.g., deep) layer of the water column. The data processing system 205can create a data structure (that can be displayed as a report) thatindicates or includes the average second layer water column transitvelocity for at least one acoustic signal.

FIG. 3 illustrates a method 300 of detecting seismic data acquisitionunit depth and acoustic signal water column transit velocity, accordingto an illustrative implementation. The method 300 can include an act ofobtaining seismic data (ACT 305). For example, the data processingsystem 205 can obtain seismic data that was acquired by the seismic dataacquisition units 140 responsive to at least one acoustic signalgenerated by the seismic source 120. The method 300 can include an actof determining the direct arrival time (ACT 310) that indicates a timeperiod from generation of the acoustic signal to detection of theseismic data by a seismic data acquisition unit 140. The method 300 caninclude an act of obtaining, e.g., by the data processing system 205, atleast one estimated depth value of at least one seismic data acquisitionunit 140, and at least one estimated water column transit velocity of atleast acoustic signal through the water column 130 (ACT 315). The method300 can include an acts of identifying, by the data processing system205, the average travel time error value (ACT 320), and determining theinitial mean absolute deviation of the travel time error (ACT 325). Themethod 300 can include the data processing system 205 applying at leastone of a depth model and a water column transit velocity model to theestimated depth value or to the estimated water column transit velocityto determine at least one updated depth value or at least one updatedwater column transit velocity (ACT 330). The method 300 can includecreating, by the data processing system 205, at least one data structureindicating the updated depth value for at least one (or for each) of theseismic data acquisition units 140, or indicating the updated watercolumn transit velocity of the acoustic signal (ACT 335).

Thus, the operations described herein can derive data acquisition unit140 depth and water column transit velocity of acoustic signals as afunction of time and depth, from the seismic data. Timing related to thereceipt of the seismic data or acoustic signal by the seismic dataacquisition units may not be absolutely known, so the systems andmethods described herein can account for or ignore timing errors whendetermining the depth values and water column transit velocities. Thedata processing system 205 can employ a constant velocity positioningsequence to determine the position of the seismic data acquisition units140 on the seabed 135.

FIG. 4 is a block diagram of a computer system 400 in accordance with animplementation of the systems and methods described herein. The computersystem or computing device 400 can include or be used to implement thesystem 100, data processing system 205, water column transit velocitygeneration module 210, depth value generation module 215, or database220.

The computing system 400 can include at least one bus 405 or othercommunication component for communicating information and at least oneprocessor 410(a-n) or processing circuit coupled to the bus 405 forprocessing information. The computing system 400 can also include one ormore processors 410 or processing circuits coupled to the bus 405 forprocessing information such as the seismic data, depth values, or watercolumn transit velocity values. The computing system 400 can includemain memory 415, such as a random access memory (RAM) or other dynamicstorage device, coupled to the bus 405 for storing information, andinstructions to be executed by the processor 410. The Main memory 415can include or be part of the database 220 and can be used for storingseismic data, depth model data, water column transit velocity modeldata, depth and updated depth values, water column transmit velocity andupdate water column transit velocity values, images, reports, executablecode, temporary variables, or other intermediate information duringexecution of instructions by the processor 410. The computing system 400may further include a read only memory (ROM) 420 or other static storagedevice coupled to the bus 405 for storing static information andinstructions for the processor 1210. A storage device 425, such as asolid state device, magnetic disk or optical disk, is coupled to the bus405 for persistently storing information and instructions. The storagedevice 425 can include or be part of the database 220.

The computing system 400 can be coupled via the bus 405 to a display 435(e.g., of the data processing system 205), such as a liquid crystaldisplay or an active matrix display, for displaying information to auser. An input device 430, such as a keyboard including alphanumeric andother keys, may be coupled to the bus 405 for communicating informationand command selections to the processor 410. The input device 430 caninclude a touch screen display 435. The input device 430 can alsoinclude a cursor control, such as a mouse, a trackball, or cursordirection keys, for communicating direction information and commandselections to the processor 410 and for controlling cursor movement onthe display 435.

The processes, systems and methods described herein can be implementedby the computing system 400 in response to the processor 410 executingan arrangement of instructions contained in main memory 415. Suchinstructions can be read into main memory 415 from anothercomputer-readable medium, such as the storage device 425. Execution ofthe arrangement of instructions contained in main memory 415 causes thecomputing system 400 to perform the illustrative processes describedherein. One or more processors in a multi-processing arrangement mayalso be employed to execute the instructions contained in main memory415. In some embodiments, hard-wired circuitry may be used in place ofor in combination with software instructions to effect illustrativeimplementations. Thus, embodiments are not limited to any specificcombination of hardware circuitry and software.

The systems and methods described herein can be implemented in digitalelectronic circuitry, or in computer software, firmware, or hardware,including the structures disclosed herein and their structuralequivalents, or in combinations of one or more of them. The subjectmatter described herein can be implemented as one or more computerprograms, e.g., one or more circuits of computer program instructions,encoded on one or more computer storage media for execution by, or tocontrol the operation of, data processing apparatus. The programinstructions can be encoded on an artificially generated propagatedsignal, e.g., a machine-generated electrical, optical, orelectromagnetic signal that is generated to encode information fortransmission to suitable receiver apparatus for execution by a dataprocessing apparatus. A computer storage medium can be, or be includedin, a computer-readable storage device, a computer-readable storagesubstrate, a random or serial access memory array or device, or acombination of one or more of them. Moreover, while a computer storagemedium is not a propagated signal, a computer storage medium can be asource or destination of computer program instructions encoded in anartificially generated propagated signal. The computer storage mediumcan also be, or be included in, one or more separate components or media(e.g., multiple CDs, disks, or other storage devices).

The operations described herein can be performed by a data processingapparatus on data stored on one or more computer-readable storagedevices or received from other sources. The term “data processingsystem” or “computing device” encompasses various apparatuses, devices,and machines for processing data, including by way of example aprogrammable processor, a computer, a system on a chip, or multipleones, or combinations of the foregoing. The apparatus can includespecial purpose logic circuitry, e.g., an FPGA (field programmable gatearray) or an ASIC (application specific integrated circuit). Theapparatus can also include, in addition to hardware, code that createsan execution environment for the computer program in question, e.g.,code that constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, a cross-platform runtimeenvironment, a virtual machine, or a combination of one or more of them.The apparatus and execution environment can realize various differentcomputing model infrastructures, such as web services, distributedcomputing and grid computing infrastructures.

A computer program (also known as a program, software, softwareapplication, app, script, or code) can be written in any form ofprogramming language, including compiled or interpreted languages,declarative or procedural languages, and it can be deployed in any form,including as a stand-alone program or as a circuit, component,subroutine, object, or other unit suitable for use in a computingenvironment. A computer program may, but need not, correspond to a filein a file system. A program can be stored (e.g., in the database 220) ina portion of a file that holds other programs or data (e.g., one or morescripts stored in a markup language document), in a single filededicated to the program in question, or in multiple coordinated files(e.g., files that store one or more circuits, subprograms, or portionsof code). A computer program can be deployed to be executed on onecomputer or on multiple computers that are located at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

While various implementations have been described and illustratedherein, those of ordinary skill in the art will readily envision avariety of other means or structures for performing the function orobtaining the results or one or more of the advantages described herein,and each of such variations or modifications is deemed to be within thescope of the inventive embodiments described herein. Parameters,dimensions, materials, and configurations described herein are meant tobe exemplary and that the actual parameters, dimensions, materials, orconfigurations may depend upon the specific application or applications.

The above-described embodiments can be implemented in any of numerousways. For example, the embodiments may be implemented using hardware,software or a combination thereof. When implemented in software, thesoftware code can be executed on any suitable processor or collection ofprocessors, whether provided in a single computer or distributed amongmultiple computers.

The implementations described herein can be embodied as a computerreadable storage medium (or multiple computer readable storage media)(e.g., a computer memory, one or more floppy discs, compact discs,optical discs, magnetic tapes, flash memories, circuit configurations inField Programmable Gate Arrays or other semiconductor devices, or othernon-transitory medium or tangible computer storage medium) encoded withone or more programs that, when executed on one or more computers orother processors, perform methods that implement the various embodimentsof the solution discussed above. The computer readable medium or mediacan be transportable, such that the program or programs stored thereoncan be loaded onto one or more different computers or other processorsto implement various aspects of the present solution as discussed above.

The terms “program” or “software” refer to any type of computer code orset of computer-executable instructions that can be employed to programa computer or other processor to implement various aspects ofembodiments as discussed herein. Additionally, one or more computerprograms that when executed perform operations described herein need notreside on a single computer or processor, but may be distributed in amodular fashion amongst a number of different computers or processors.

Data structures may be stored in computer-readable media in any suitableform. Data structures may be shown to have fields that are relatedthrough location in the data structure. Such relationships may likewisebe achieved by assigning storage for the fields with locations in acomputer-readable medium that convey relationship between the fields.However, pointers, tags or other mechanisms can be used to establish arelationship between information in fields of a data structure, orbetween data elements.

While operations are depicted in the drawings in a particular order,such operations are not required to be performed in the particular ordershown or in sequential order, and all illustrated operations are notrequired to be performed. Actions described herein can be performed in adifferent order.

The separation of various system components does not require separationin all implementations, and the described program components can beincluded in a single hardware or software product. For example, thewater column transit velocity generation module 210 or the depth valuegeneration module 215 can be a single module, a logic device having oneor more processing circuits, or part of one or more servers of the dataprocessing system 205.

Having now described some illustrative implementations, it is apparentthat the foregoing is illustrative and not limiting, having beenpresented by way of example. In particular, although many of theexamples presented herein involve specific combinations of method actsor system elements, those acts and those elements may be combined inother ways to accomplish the same objectives. Acts, elements andfeatures discussed in connection with one implementation are notintended to be excluded from a similar role in other implementations orimplementations.

The phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including” “comprising” “having” “containing” “involving”“characterized by” “characterized in that” and variations thereofherein, is meant to encompass the items listed thereafter, equivalentsthereof, and additional items, as well as alternate implementationsconsisting of the items listed thereafter exclusively. In oneimplementation, the systems and methods described herein consist of one,each combination of more than one, or all of the described elements,acts, or components.

Any references to implementations or elements or acts of the systems andmethods herein referred to in the singular may also embraceimplementations including a plurality of these elements, and anyreferences in plural to any implementation or element or act herein mayalso embrace implementations including only a single element. Referencesin the singular or plural form are not intended to limit the presentlydisclosed systems or methods, their components, acts, or elements tosingle or plural configurations. References to any act or element beingbased on any information, act or element may include implementationswhere the act or element is based at least in part on any information,act, or element.

Any implementation disclosed herein may be combined with any otherimplementation or embodiment, and references to “an implementation,”“some implementations,” “an alternate implementation,” “variousimplementations,” “one implementation” or the like are not necessarilymutually exclusive and are intended to indicate that a particularfeature, structure, or characteristic described in connection with theimplementation may be included in at least one implementation orembodiment. Such terms as used herein are not necessarily all referringto the same implementation. Any implementation may be combined with anyother implementation, inclusively or exclusively, in any mannerconsistent with the aspects and implementations disclosed herein.

References to “or” may be construed as inclusive so that any termsdescribed using “or” may indicate any of a single, more than one, andall of the described terms. References to “at least one” of a list ofelements indicates an inclusive OR combination of the listed elements.

Where technical features in the drawings, detailed description or anyclaim are followed by reference signs, the reference signs have beenincluded to increase the intelligibility of the drawings, detaileddescription, and claims. Accordingly, neither the reference signs northeir absence have any limiting effect on the scope of any claimelements.

The systems and methods described herein may be embodied in otherspecific forms without departing from the characteristics thereof. Theforegoing implementations are illustrative rather than limiting of thedescribed systems and methods. For example, the marine seismic surveyscan include ocean, sea, lake, transit-zone, salt water, fresh water, ormixed water surveys. Scope of the systems and methods described hereinis thus indicated by the appended claims, rather than the foregoingdescription, and changes that come within the meaning and range ofequivalency of the claims are embraced therein.

What is claimed is:
 1. A method of detecting seismic data acquisitionunit depth and acoustic signal water column transit velocity for aseismic survey, comprising: obtaining, by a data processing system,seismic data acquired by a plurality of seismic data acquisition unitsdisposed on a seabed responsive to an acoustic signal propagated from anacoustic source through a water column; determining, by the dataprocessing system, from the seismic data, a direct arrival time for theacoustic signal at each of the plurality of seismic data acquisitionunits; obtaining an estimated depth value of each of the plurality ofseismic data acquisition units and an estimated water column transitvelocity of the acoustic signal; identifying an average travel timeerror value based on the direct arrival time for the acoustic signal ateach of the plurality of seismic data acquisition units, the estimateddepth value, and the estimated water column transit velocity;determining an initial mean absolute deviation of travel time error fromthe average travel time error value; applying a depth model and a watercolumn transit velocity model to the estimated depth value and to theestimated water column transit velocity to determine, using the initialmean absolute deviation of travel time error, an updated depth value andan updated water column transit velocity for each of the plurality ofseismic data acquisition units; and creating, by the data processingsystem, a data structure indicating the updated depth value for each ofthe plurality of seismic data acquisition units.
 2. The method of claim1, comprising: providing a rendering of the data structure for displayat a computing device.
 3. The method of claim 1, comprising: identifyinga first layer water column transit velocity for the acoustic signal andeach of the plurality of seismic data acquisition units; determining asecond layer water column transit velocity for the acoustic signal andeach of the plurality of seismic data acquisition units based on thefirst layer water column transit velocity and the updated water columntransit velocity.
 4. The method of claim 3, wherein the water columnincludes a first layer and a second layer, comprising: determining anaverage second layer water column transit velocity for the acousticsignal based on the second layer water column transit velocity for theacoustic signal and each of the plurality of seismic data acquisitionunits; and identifying a time variant second layer water column transitvelocity for the second layer of the water column, wherein the secondlayer of the water column is shallower than the first layer of the watercolumn.
 5. The method of claim 4, comprising: creating the datastructure including the average second layer water column transitvelocity for the acoustic signal.
 6. The method of claim 1, whereinapplying the depth model and the water column transit velocity model tothe estimated depth value and to the estimated water column transitvelocity comprises: determining an updated travel time error between anestimated arrival time and the direct arrival time for each of theplurality of seismic data acquisition units; determining an updatedaverage travel time error; determining an updated mean absolutedeviation using the updated travel time error.
 7. The method of claim 1,wherein the updated depth value is a first updated depth value, and theupdated water column transit velocity is a first water column transitvelocity, comprising: identifying an updated mean absolute deviation;determining that the updated mean absolute deviation is less than theinitial mean absolute deviation; replacing the first updated depth valuewith a second updated depth value; and replacing the first water columntransit velocity with a second water column transit velocity.
 8. Themethod of claim 1, wherein the updated depth value is a first updateddepth value, and the updated water column transit velocity is a firstwater column transit velocity, comprising: identifying a second updateddepth value and a second water column transit velocity; determining anupdated mean absolute deviation; determining that the updated meanabsolute deviation is greater than the initial mean absolute deviation;discarding the second updated depth value; and discarding the secondwater column transit velocity.
 9. The method of claim 1, comprising:adjusting the estimated depth value to identify an updated estimateddepth value; and applying the depth model to the updated estimated depthvalue to determine the updated depth value.
 10. The method of claim 1,comprising: adjusting the estimated water column transit velocity toidentify an updated estimated depth value; and applying the water columntransit velocity model to the updated estimated depth value to determinethe updated depth value.
 11. The method of claim 1, comprising:deploying the seismic data acquisition units from a vessel to a seabed.12. The method of claim 1, comprising: obtaining, by the seismic dataacquisition units, the seismic data.
 13. The method of claim 1,comprising: obtaining, by the data processing system, the seismic dataresponsive to a plurality of acoustic signals propagated from aplurality of seismic sources.
 14. The method of claim 1, comprising:actuating a seismic source to generate the acoustic signal.
 15. A systemof detecting parameters related to a marine seismic survey, comprising:a data processing system having a depth value generation module and awater column transit velocity generation module, the data processingsystem configured to: obtain seismic data acquired by a plurality ofseismic data acquisition units disposed on a seabed responsive to anacoustic signal propagated from an acoustic source through a watercolumn; determine, from the seismic data, a direct arrival time for theacoustic signal at each of the plurality of seismic data acquisitionunits; obtain an estimated depth value of each of the plurality ofseismic data acquisition units and an estimated water column transitvelocity of the acoustic signal; identify an average travel time errorvalue based on the direct arrival time for the acoustic signal at eachof the plurality of seismic data acquisition units, the estimated depthvalue, and the estimated water column transit velocity; determine aninitial mean absolute deviation of travel time error from the averagetravel time error value; apply a depth and water column transit velocitymodel to the estimated depth value and to the estimated water columntransit velocity determine an updated depth value and an updated watercolumn transit velocity for each of the plurality of seismic dataacquisition units; and create a data structure indicating the updateddepth value for each of the plurality of seismic data acquisition units.16. The system of claim 15, wherein the plurality of seismic dataacquisition units acquire the seismic data.
 17. The system of claim 15,comprising: a seismic source that generates the acoustic signal.
 18. Thesystem of claim 15, comprising the data processing system configured to:determine an updated travel time error between an estimated arrival timeand the direct arrival time for each of the plurality of seismic dataacquisition units; determine an updated average travel time error; anddetermine an updated mean absolute deviation using the updated traveltime error.
 19. The system of claim 15, wherein the updated depth valueis a first updated depth value, and the updated water column transitvelocity is a first water column transit velocity, comprising the dataprocessing system configured to: identify an updated mean absolutedeviation; determine that the updated mean absolute deviation is lessthan the initial mean absolute deviation; replace the first updateddepth value with a second updated depth value; and replace the firstwater column transit velocity with a second water column transitvelocity.
 20. A computer readable storage medium storing instructionsthat when executed by one or more data processors, cause the one or moredata processors to perform operations comprising: obtaining seismic dataacquired by a plurality of seismic data acquisition units disposed on aseabed responsive to an acoustic signal propagated from an acousticsource through a water column as part of a seismic survey; determining,from the seismic data, a direct arrival time for the acoustic signal ateach of the plurality of seismic data acquisition units; obtaining anestimated depth value of each of the plurality of seismic dataacquisition units and an estimated water column transit velocity of theacoustic signal; identifying an average travel time error value based onthe direct arrival time for the acoustic signal at each of the pluralityof seismic data acquisition units, the estimated depth value, and theestimated water column transit velocity; determining an initial meanabsolute deviation of travel time error from the average travel timeerror value; determining an updated depth value and an updated watercolumn transit velocity for each of the plurality of seismic dataacquisition units; and creating a data structure indicating the updateddepth value for each of the plurality of seismic data acquisition units.